Dimensions of knowledge quality

In Knowlege Quality Assessment three dimensions of quality can be distinguished (Maxim and Van der Sluijs. forthcoming):

• substantive (referring to the content of the knowledge itself)
• contextual (referring to the context of knowledge production or use, i.e., “when and where” knowledge is framed, produced, communicated or used, in which socio-economic and political conditions)
• procedural (referring to the processes of knowledge framing, production, communication or use, i.e., the “how” question)

Scientific knowledge of good substantive quality can have a poor procedural quality, and therefore it will be little trusted by the policy-makers, or a low contextual quality, and in this case it will not lead to improvement of decision-making as the scale of the information is different from the scale of the action. Similarly, a procedurally robust knowledge production, adapted to the context, can be a substantively low quality information (e.g., technically inexact or methodologically unreliable).

The contextual dimension refers to the influence of the spatial, socioeconomic and political contexts on the knowledge. In decision-making, scientific knowledge has to be “put in context” for adapting it to the specific problem under discussion, mainly the relevant scale (local? regional? continental?), the socioeconomic stakes (child mortality? income losses?) and the options of action (ban? limit uses?). The context also gives sense to a piece of knowledge, allowing to interpret data, measurements, models, to say “what do they mean”. At the same time, the context can be a source of uncertainty through constraints on the possibilities of producing knowledge or of using the knowledge available.

The importance of procedural dimension of quality is often underestimated. Despite the fact that it does not refer to the content of the knowledge itself, the processes through which knowledge is framed, produced and communicated can significantly influence the confidence of stakeholders and policymakers in scientific results. Value-ladenness is particularly important in science for policy. Today, several stakeholders (industry, academia, NGOs, interest groups, non-scientific experts, investigation journalists, etc.) produce knowledge involved, or for getting involved, in decision processes. It is therefore irrelevant to talk about “science” in general, as science is not an undifferentiated “whole”. The process of knowledge production depends on elements such as the researchers’ independency, the source of his/her funding, the stakes of the institutions employing him/her. These elements are all playing important role in the perception of knowledge quality and therefore, indirectly, influence the decision-making. If uncertainty assessment is to contribute to the quality of decision-making and to the confidence in scientific results, these aspects must be explicitly addressed in uncertainty analysis. One of the most important aspects of value-ladenness in all the steps of knowledge cycle is conflict of interest, because in many controversies it represents a major source of lack of confidence in scientific results.
 

Reference
L. Maxim and J.P. van der Sluijs (forthcoming) Quality in environmental science for policy: assessing uncertainty as component of policy analysis.